The terrestrial observation missions carried out by a spacecraft consist in acquiring images of terrestrial areas, i.e. areas located on the surface of the Earth, in response to requests from clients. In particular, such a spacecraft follows a traveling orbit around the Earth so as to be capable of acquiring said terrestrial areas as it passes over them for a predetermined duration. Additionally, the increase in agility of such a spacecraft allows the number of terrestrial areas that are imaged to be increased, these areas now potentially being located on either side of said orbit or else acquired at various angles over the course of multiple orbits. Thus, any instant in time along said traveling orbit corresponds to one or more opportunities for acquiring images of different terrestrial areas.
Said requests are received regularly by the spacecraft, generally daily. Currently, the requests for such acquisitions of terrestrial areas are increasing in number, since they are no longer limited just to those industrial sectors that are historically linked to the space imaging sector. For example, and non-limitingly, the agriculture sector now makes considerable use of terrestrial observations with a view to optimizing the use of agrarian areas.
Consequently, the requests to be processed by such a spacecraft are continually increasing both in terms of number and in terms of complexity, inasmuch as these requests contain highly specific constraints that are associated with the terrestrial area to be acquired, such as for example localization or light exposure conditions, or else the requirement for multiple acquisitions with a view to obtaining stereo, tri-stereo or multispectral images. In addition to this is the fact that requests can be differentiated according to their level of priority.
The management of the constraints that are associated with said requests must also be performed in parallel to the management of the operational constraints that are associated with said spacecraft, whether they be cumulative (memory size, consumed electrical power, maximum operating time of on-board instruments), or else local (minimum duration between two successive acquisitions).
It is therefore understood that the aim consisting in fulfilling a set of such requests is a very highly constrained problem for which it is necessary to plan the imaging to be performed over time, according to the orbit of said spacecraft. More broadly, such a problem comes under the category of “traveling salesman” optimization problems, which are well known for being very difficult to solve in a reasonable amount of time.
The problem is particularly critical in the case in which the user request must be fulfilled within a short timeframe. Increasing the number of satellites makes it possible to have satellite access to an area within much shorter timeframes. However, it is only possible to make full use of this capability if the acquisition plan can also be updated within short timeframes.
Although it is known that high-quality approximation solutions to such a planning problem can be provided by virtue of heuristic optimization techniques (for example greedy algorithms), their computation times are not satisfactory considering the volume of requests to be processed under operational conditions.
More recently, problem-solving techniques based on important simplification assumptions, in particular regarding the decoupled management of the local and cumulative constraints of said craft, have made it possible to provide solutions to the management of said local constraints within a reasonable amount of time, which is a first step toward completely solving said planning problem. Although some of these techniques do indeed allow cumulative constraints to be taken into account, none of them allows the acquisition of stereo, tri-stereo or multispectral images to be taken into account, in particular in the case of an observation satellite operating in pushbroom mode. As such, these techniques are limiting and not optimal with regard to the number of requests that they are able to fulfill.